*! version 1.2 March 2021
help qregsel

Title

Quantile Regression Corrected for Sample Selection

Syntax

qregsel
depvar varlist
[if] [in]
,
select([depvar_s =] varlist_s)
quantile(#)
[
copula(copula)
noconstant
finergrid
coarsergrid
rescale
nodots
]

Main
select() specifies a selection equation.

quantile(#) specifies the quantiles to be estimated.

copula(copula) specifies a copula;
default is gaussian.

noconstant suppress a constant term in the outcome equation.

finergrid find the value of the copula parameter using a grid of 199 values instead of 100, as done by default.

coarsergrid find the value of the copula parameter using a grid of 50 values instead of 100, as done by default.

rescale rescale the regressors in the outcome equation.

nodots suppress progress dots.

Description

qregsel estimates a copula-based sample selection model for quantile regression.
Users can specify a copula from the lists below.

Available copulas are gaussian and frank.

Notes: The name of the copula is case-sensitive.

Options

Main

select() is required. It specifies a selection equation.
If depvar_s is specified, it should be coded as 0 or 1, which 0 indicating depvar not observed for an observation
and 1 indicating depvar observed for an observation.

quantile(#) is required. It specifies a set of quantiles to be estimated.

copula(copula) specifies a copula function for the dependence between outcome and selection equation.
See above for the list of available copulas.
Default is gaussian.

nonconstant suppresses a constant term of the outcome equation.

finergrid find the value of the copula parameter using a grid of 199 values instead of 100, as done by default. These values are chosen such that U and V have a rank correlation between -.99 and .99.

coarsergrid find the value of the copula parameter using a grid of 50 values instead of 100, as done by default. These values are chosen such that U and V have a rank correlation approximately between -.99 and .97.

rescale rescale the regressors of the outcome equation substracting sample mean and dividing by standard deviation..

Saved results

qregsel saves the following in e():

Scalars

e(N) number of observations

e(N_selected) number of selected observations

e(rho) copula parameter

e(kendall) Kendall's tau

e(spearman) Spearman's rank correlation

Macros

e(copula) specified copula

e(depvar) dependent variable

e(indepvar) independent variables

e(cmdline) command line

e(outcome_eq) outcome equation

e(select_eq) selection equation

e(cmd) qregsel

e(predict) predict command name

e(rescale) use of rescale option

e(title) title in estimation output

e(properties) b

Matrices

e(b) coefficients matrix

e(grid) matrix with the values of the objective function for each value of rho, and its respective Spearman rank correlation and Kendall's tau

e(coefs) coefficient matrix. Each column corresponds to the coefficients for a quantile

Functions

e(sample) marks estimation sample

Examples

. webuse womenwk, clear
. qregsel wage educ age, select(married children educ age) quantile(.1 .5 .9)
. ereturn list

References

Arellano, M., and S. Bonhomme. 2017.
"Quantile Selection Models With an Application to Understanding Changes in Wage Inequality." Econometrica 85(1): 1–28.

Authors

Ercio Munoz, CUNY Graduate Center, New York, US.

Email: emunozsaavedra@gc.cuny.edu

Mariel Siravegna, Georgetown University, Washington DC, US.

Email: mcs92@georgetown.edu

Also see

Online: heckman, qreg, heckmancopula